Machine health monitoring is an indispensable part of condition-based maintenance (CBM). Knowing machine health at any given time helps to minimize unexpected downtimes, optimize maintenance schedule, maximize mission readiness, increase safety, and, ultimately, reduce life-cycle costs. Fault detection is one of the key enablers of machine condition monitoring.
Fault detection has been conventionally treated as a classification problem, i.e., to classify machine health status into either normal or abnormal (faulty) conditions. This classification design requires well-distributed data samples representing both normal condition and different faulty conditions. In most real-world applications data samples for normal condition are readily available. However, data samples for abnormal conditions are not. Data samples associated with abnormal condition are difficult or costly to obtain. This is compounded by the need to obtain a sufficient number of data samples concerning all different faulty conditions.
A more cost-effective design strategy designs a fault detection system using data samples based on normal condition only. Under this design strategy, a model that accurately characterizes normal behavior is developed. A faulty condition is declared when system behavior deviates by a predefined amount from the model-described normal behavior. Such design strategy is known as “novelty detection”. There are numerous approaches for novelty detection, ranging from statistics to neural networks.
Prior systems may for example disclose a method for ascertaining anomalies in an electric motor by computing and processing a set of fast Fourier Transforms (FFTs) of the supply current waveforms for a motor known to be in a normal condition. The method includes a step of clustering the input vectors into several cluster groups using Ward's method and outputting anomaly warning signal if a new input sample for a motor under supervision is not inside any of these clusters.
Prior systems within the area of analyzing fault logs disclose methods that aid a field engineer in analyzing fault logs of a malfunctioning machine and determining causes and/or recommended repair actions.
Thus, improved and efficient means to identifying a normal operation envelope may be required.